TY - GEN

T1 - Infrastructure state transition probability computation using duration models

AU - Mishalani, Rabi G.

AU - Madanat, Samer M.

PY - 2002

Y1 - 2002

N2 - Sound infrastructure deterioration models are essential for accurately predicting future conditions which, in turn, are key inputs to effective maintenance and rehabilitation decision-making. The challenge central to developing accurate deterioration models is that condition is often measured on a discrete scale, such as inspectors' ratings. Furthermore, deterioration is a stochastic process that varies widely with several factors, many of which are generally not captured by available data. Therefore, probabilistic discrete state models are often used to characterize deterioration. Such models are based on transition probabilities which capture the nature of the evolution of condition states from one time point to the next. However, current methods for determining such probabilities suffer from several serious limitations. An alternative approach addressing these limitations is presented in this paper. A probabilistic model of the time spent in a state is derived and the approach used for estimating its parameters is described. Furthermore, a methodology for determining the corresponding state transition probabilities from the developed duration model is presented. Finally, the overall methodology is demonstrated using a data set of reinforced concrete bridge deck observations.

AB - Sound infrastructure deterioration models are essential for accurately predicting future conditions which, in turn, are key inputs to effective maintenance and rehabilitation decision-making. The challenge central to developing accurate deterioration models is that condition is often measured on a discrete scale, such as inspectors' ratings. Furthermore, deterioration is a stochastic process that varies widely with several factors, many of which are generally not captured by available data. Therefore, probabilistic discrete state models are often used to characterize deterioration. Such models are based on transition probabilities which capture the nature of the evolution of condition states from one time point to the next. However, current methods for determining such probabilities suffer from several serious limitations. An alternative approach addressing these limitations is presented in this paper. A probabilistic model of the time spent in a state is derived and the approach used for estimating its parameters is described. Furthermore, a methodology for determining the corresponding state transition probabilities from the developed duration model is presented. Finally, the overall methodology is demonstrated using a data set of reinforced concrete bridge deck observations.

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U2 - 10.1061/40632(245)64

DO - 10.1061/40632(245)64

M3 - Conference contribution

AN - SCOPUS:0036052730

SN - 0784406324

SN - 9780784406328

T3 - Proceedings of the International Conference on Applications of Advanced Technologies in Transportation Engineering

SP - 505

EP - 512

BT - Proceedings of the International Conference on Applications of Advanced Technologies in Transportation Engineering

PB - ASCE - American Society of Civil Engineers

T2 - Proceedings of the seventh International Conference on: Applications of Advanced Technology in Transportation

Y2 - 5 August 2002 through 7 August 2002

ER -